Opening necessary in the project libraries

Part 1

Showing how many dollars are spent by country on different diseases.

## Importing Data Stored outside of Project

spending <-
  read_csv(
    "/Users/magda/Documents/EPFL/IHME_DAH_DATABASE_1990_2019_Y2020M04D23.CSV",
    na = c('NA', '-', '')
  )

spending_2019 <- spending %>%
  filter(year == 2019) %>%
  mutate(source = str_replace(source, "_", " ")) %>%
  dplyr::select(source, channel, recipient_country,
                contains(c("hiv", "mal", "tb", "ncd")))

## Importing map

world_border <-
  sf::st_read('Resources/ne_50m_admin_0_countries/ne_50m_admin_0_countries.shp') %>%
  clean_names()

## Reading layer `ne_50m_admin_0_countries' from data source `/Users/magda/Documents/EPFL/Final_Project_2/Report_3/Resources/ne_50m_admin_0_countries/ne_50m_admin_0_countries.shp' using driver `ESRI Shapefile'
## Simple feature collection with 241 features and 94 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -89.99893 xmax: 180 ymax: 83.59961
## Geodetic CRS:  WGS 84

## Expenditure per country

spending_short <- spending_2019 %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(source) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_short  <-  world_border  %>%
  left_join(spending_short, c("name_long" = "source")) %>%
  st_transform(crs = 'ESRI:54011')

## Creating a map

tm_shape(world_short) +
  tm_polygons(col = "expenditure",
              style = "quantile",
              palette = "-plasma") +
  tm_polygons(col = "#2887a1")

All presented in this report information comes from the IHME Development Assistance for Health Database which includes data on funds received and donated from/for specific countries over the years. On the above map we can see how many dollars were spent by different countries to deal with diseases in 2019. Some of the countries like US, Canada, Germany or Japan seems to participated with higher funding, but it seems also like many countries didn’t donate money for this reason at all.

Question 2

Showing how many dollars are received by countries.

## Importing data for 2015

spending_2015 <- spending %>%
  filter(year == 2015) %>%
  mutate(source=str_replace(source,"_", " ")) %>%
   dplyr:: select(
    year, source, channel, recipient_country,
    contains(c("hiv", "mal", "tb", "ncd")))

## Receivings per country

received_money <- spending_2015 %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(recipient_country) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_received  <-  world_border  %>% 
  left_join(received_money, c("name_long" = "recipient_country")) %>%
  st_transform(crs = 'ESRI:54011')

## Creating a map  

map_received <- tm_shape(world_received) +
tm_polygons(col = "expenditure", 
              style = "quantile",
            palette = "-viridis") +
  tm_polygons(col = "#2887a1")

map_received

On the other hand when we look on the next map we can see that many coutries received money to deal with different diseases. Especially we can highlight here Africa, South Americe and south part of Asia.

Question 3

Showing how many dollars are received by regions.

## Adding gbd_regions to map

gbd_regions <- spending %>%
  mutate(source = str_replace(source, "_", " ")) %>%
  dplyr::select(recipient_country, gbd_region) %>%
  distinct()

world_gbd <-  world_border  %>%
  left_join(gbd_regions, c("name_long" = "recipient_country"))

## Preparing data

gbd_regions_data <- spending %>%
  filter(year == 2015) %>%
  mutate(source = str_replace(source, "_", " ")) %>%
  dplyr::select(recipient_country, gbd_region, gbd_region_id,
                contains(c("hiv", "mal", "tb", "ncd")))

received_gbd <- gbd_regions_data %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(gbd_region) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_gbd <-  world_gbd  %>%
  left_join(received_gbd, c("gbd_region" = "gbd_region")) %>%
  st_transform(crs = 'ESRI:54011')

world_gbd <- world_gbd %>%
  group_by(gbd_region) %>%
  summarise(sum_exp = sum(expenditure))

## Creating map

tm_shape(world_gbd) +
  tm_polygons(col = "sum_exp",
              style = "quantile",
              palette = "-viridis") +
  tm_polygons(col = "#2887a1")

Very similar to the previous map, but this time we can see how much money different regions received. When we combined data it became even more visible that majority of the funds went to Africa.

Question 4 (optional)

Creating an animated time series map.

spending_last10 <- spending %>%
  mutate(source=str_replace(source,"_", " ")) %>%
  filter(year > 2009) %>%
     dplyr:: select(
    year, source, channel, recipient_country,
    contains(c("hiv", "mal", "tb", "ncd")))

# 2010

## Preparing data

received_money_2010 <- spending_last10 %>%
  filter(year == 2010) %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(recipient_country) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_received_2010  <-  world_border  %>% 
  left_join(received_money_2010, c("name_long" = "recipient_country")) 

## Creating a map  

map_2010 <- tm_shape(world_received_2010) +
tm_polygons(col = "expenditure", 
              style = "quantile",
            palette = "-viridis") +
  tm_polygons(col = "#2887a1")+
  tm_layout(title = 2010)

# 2011

## Preparing data

received_money_2011 <- spending_last10 %>%
  filter(year == 2011) %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(recipient_country) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_received_2011  <-  world_border  %>% 
  left_join(received_money_2011, c("name_long" = "recipient_country")) 

## Creating a map  

map_2011 <- tm_shape(world_received_2011) +
tm_polygons(col = "expenditure", 
              style = "quantile",
            palette = "-viridis") +
  tm_polygons(col = "#2887a1")+
  tm_layout(title = 2011)

# 2012

## Preparing data

received_money_2012 <- spending_last10 %>%
  filter(year == 2012) %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(recipient_country) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_received_2012  <-  world_border  %>% 
  left_join(received_money_2012, c("name_long" = "recipient_country")) 


## Creating a map  

map_2012 <- tm_shape(world_received_2012) +
tm_polygons(col = "expenditure", 
              style = "quantile",
            palette = "-viridis") +
  tm_polygons(col = "#2887a1") +
  tm_layout(title = 2012)

# 2013

## Preparing data

received_money_2013 <- spending_last10 %>%
  filter(year == 2013) %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(recipient_country) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_received_2013  <-  world_border  %>% 
  left_join(received_money_2013, c("name_long" = "recipient_country")) 

## Creating a map  

map_2013 <- tm_shape(world_received_2013) +
tm_polygons(col = "expenditure", 
              style = "quantile",
            palette = "-viridis") +
  tm_polygons(col = "#2887a1")+
  tm_layout(title = 2013)


# 2014

## Preparing data

received_money_2014 <- spending_last10 %>%
  filter(year == 2014) %>%
  pivot_longer(cols = contains(c("hiv", "mal", "tb", "ncd"))) %>%
  mutate(disease = str_extract(name, "[^_]+")) %>%
  group_by(recipient_country) %>%
  summarise(expenditure = sum(value, na.rm = TRUE)) %>%
  ungroup()

## Combining data

world_received_2014  <-  world_border  %>% 
  left_join(received_money_2014, c("name_long" = "recipient_country")) 

## Creating a map  

map_2014 <- tm_shape(world_received_2014) +
tm_polygons(col = "expenditure", 
              style = "quantile",
            palette = "-viridis") +
  tm_polygons(col = "#2887a1") +
  tm_layout(title = 2014)

map_2010

map_2011

map_2012

map_2013

map_2014

On the above animation we can see how much money countries received between year 2010 and 2014. Over the years, one of the biggest drop in donations we can notice for China and Mongolia.

Question 5

Creating an interactive map

tmap_mode("view")
map_received

tmap_save(map_received, "map_received.html")

Above interactive map enables to check accurate values of donation received per country.

Question 6

Focusing the map on one continent only.
Creating a visualisation with an inset map.

## Importing map for Africa

africa_border <-
  sf::st_read('Resources/ne_50m_admin_0_countries/ne_50m_admin_0_countries.shp') %>%
  clean_names() %>%
  filter(continent == "Africa")

## Reading layer `ne_50m_admin_0_countries' from data source `/Users/magda/Documents/EPFL/Final_Project_2/Report_3/Resources/ne_50m_admin_0_countries/ne_50m_admin_0_countries.shp' using driver `ESRI Shapefile'
## Simple feature collection with 241 features and 94 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -89.99893 xmax: 180 ymax: 83.59961
## Geodetic CRS:  WGS 84

africa_received_2015  <-  africa_border  %>%
  left_join(received_money, c("name_long" = "recipient_country"))

## Creating the main map

tmap_mode("plot")

africa_received_2015 <- africa_received_2015 %>%
  mutate(
    categories = case_when(
      expenditure < 10000 ~ "< 10 k",
      expenditure >= 10000 & expenditure < 100000 ~ "10k to 100k",
      expenditure >= 100000 &
        expenditure < 500000 ~ "100k to 0.5 mln",
      expenditure >= 500000 &
        expenditure < 1000000 ~ "0.5 mln to 1 mln",
      expenditure >= 1000000 ~ "> 1mln"
    )
  ) %>%
  mutate(categories = factor(
    categories,
    levels = c("< 10 k",
               "10k to 100k",
               "100k to 0.5 mln",
               "0.5 mln to 1 mln",
               "> 1mln")
  ))

africa_map <- ggplot() +
  geom_sf(
    data = africa_border,
    fill = "transparent",
    color = "grey",
    size = 0.5
  ) +
  geom_sf(
    data = africa_received_2015,
    aes(fill = categories),
    alpha = 1,
    color = "lightgrey",
    size = 0.1
  ) +
  theme_void() +
  scale_fill_carto_d(palette = "BurgYl") +
  guides(
    fill = guide_legend(
      title =  "Received funds",
      label.position = "bottom",
      title.position = "top",
      nrow = 1
    )
  ) +
  theme(legend.position = "bottom") +
    labs( title = "Funds received for dealing with diseases")


## Creating the inset map

africa_rectangle <- world_border %>%
  filter(continent %in% "Africa") %>%
  st_bbox() %>%
  st_as_sfc()

africa_inset_map <- ggplot() +
  geom_sf(
    data = world_border,
    fill = "white",
    size = 0.3,
    colour = "lightgrey"
  ) +
  geom_sf(
    data = africa_rectangle,
    fill = NA,
    color = "red",
    size = 0.8
  ) +
  theme_void()

## Adding maps together

layout <- c(patchwork::area(
  t = 0,
  l = 1,
  b = 7,
  r = 9
),
patchwork::area(
  t = 5,
  l = 2,
  b = 6,
  r = 5
))

africa_map + africa_inset_map + plot_layout(design = layout)

Above visualization presents how many dollars countries in Africa received. Looking at the smaller area enables us see more precisily how much each country got and how it differs.

Question 8

Plotting selected city map with 5 features.

## Preparing features

road <- opq(c(8.482299, 47.332298, 8.601518, 47.399211)) %>%
  add_osm_feature(key = "highway",
                  value = c("motorway", "primary",
                            "secondary", "tertiary")) %>%
  osmdata_sf()

river <- opq(c(8.482299, 47.332298, 8.601518, 47.399211)) %>%
  add_osm_feature(key = "waterway", value = "river") %>%
  osmdata_sf()

club <- opq(c(8.482299, 47.332298, 8.601518, 47.399211)) %>%
  add_osm_feature(key = "amenity", value = "nightclub") %>%
  osmdata_sf()

lake <- opq(c(8.482299, 47.332298, 8.601518, 47.399211)) %>%
  add_osm_feature(key = "natural", value = "water") %>%
  osmdata_sf()

bar <- opq(c(8.482299, 47.332298, 8.601518, 47.399211)) %>%
  add_osm_feature(key = "amenity", value = "bar") %>%
  osmdata_sf()

restaurant <- opq(c(8.482299, 47.332298, 8.601518, 47.399211)) %>%
  add_osm_feature(key = "amenity", value = "restaurant") %>%
  osmdata_sf()

## Creating map of Zurich

ggplot() +
  geom_sf(data = river$osm_lines,
          inherit.aes = FALSE,
          color = "blue") +
  geom_sf(data = lake$osm_lines,
          inherit.aes = FALSE,
          color = "steelblue") +
  geom_sf(data = road$osm_lines,
          inherit.aes = FALSE,
          color = "#bdbdbd") +
  geom_sf(data = restaurant$osm_points,
          inherit.aes = FALSE,
          aes(color = "#ffff33"),
          show.legend = "point") +
  geom_sf(data = bar$osm_points,
          inherit.aes = FALSE,
          aes(color = "#984ea3"),
          show.legend = "point") +
  geom_sf(data = club$osm_points,
          inherit.aes = FALSE,
         aes(color = "#31a354"),
          show.legend = "point") +
  coord_sf(
    xlim = c(8.482299, 8.601518),
    ylim = c(47.332298, 47.399211),
    expand = FALSE
  ) +
  theme_void() +
  scale_color_manual (values = c("#ffff33", "#984ea3","#31a354"), label=c("Restaurants","Bars","Clubs")) +
  theme(plot.title = element_text(size = 20, face = "bold"),
        legend.title = element_blank()) +
  labs(title = "ZURICH",
       subtitle = "Map of Restaurants, Bars and Clubs")

This city map of Zurich presents where resturants, bars and clubs are located. Additionaly highlights rivers, roads and Zurich lake. We can notice that number of clubs outweight the map.